Factors Predicting Depressive Symptoms Among Thai High School Students
Bibliographic record
Abstract
Depression is a mental health issue among high school students. This cross-sectional study examines depressive symptoms and determines the factors predicting it among anonymous Thai high school students. A total of 404 students, with an average age of 14.89 years (SD = 1.66), were selected using a multi-stage sampling technique was employed at an autonomous high school located in Nakhon Pathom, Thailand, during the first semester of the 2023 academic year. A self-administered questionnaire was used to collect the potential factors which had a consistency reliability coefficient of 0.75, and a 9-item patient health questionnaire which had a consistency reliability coefficient of 0.85. Descriptive statistics and stepwise multiple regression analysis were used to analyze the data. The mean score of depressive symptoms was 8.85 (SD = 5.25), which indicates no risk. Four factors were consistently associated with depressive symptoms, with being female the highest significant predictor (β = .251), followed by academic achievement (β = -.167), self-management behaviors (β = -.159), and attitudes towards mental health problems (β = -.143). These four predictors accounted for 20.3% of the variance in depressive symptoms in high school students (F4, 403 = 10.715, p < .001). These results indicate that educators and school personnel should implement targeted programs or interventions for high school students. Such initiatives are likely to enhance academic performance, foster self-regulation skills, and encourage constructive attitudes toward mental health issues. This approach may be particularly beneficial for female students, potentially reducing the risk of developing depressive symptoms in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".